Intelligent optimal layout of drainage pipe network monitoring points based on information entropy theory

Min He, Yibo Zhang, Zhaoxi Ma, Qinnan Zhao
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Abstract

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.
基于信息熵理论的排水管网监测点智能优化布局
在经济发展的推动下,城市排水管网迅速扩张,给高效监测和管理带来了巨大挑战。这些管网的复杂性和规模使得城市生活污水、雨水和工业废水的排放难以得到有效监控和管理,从而导致非法排放、渗漏、环境污染和经济损失。有效的管理有赖于排水管网监测点的合理布局。然而,现有关于优化监测点布局的研究十分有限,主要依赖于人工分析和模糊聚类方法,这容易造成人为偏差和监测数据无效。针对这些局限性,本研究提出了一种耦合模型方法,用于自动优化排水管网中的监测点布设。所提出的模型集成了信息熵指数、贝叶斯推理、蒙特卡罗方法和雨水管理模型(SWMM),可客观、可测量地优化监测点的布设。信息熵算法用于量化排水管网的不确定性和复杂性,有助于确定最佳监测点位置。贝叶斯推理法用于根据观测数据更新概率,而蒙特卡罗法则用于生成不确定参数的概率分布。利用 SWMM 模拟排水管网内的雨水径流和污染物迁移。结果表明:(1) 污染源跟踪模型参数反演模拟结果的相对平均误差与信息熵成线性拟合关系。计算表明,二者之间存在良好的正线性相关关系,验证了信息熵算法在监测节点优化领域的可行性;(2)信息熵算法可以很好地应用于单个监测节点和多个监测节点的优化布局,并能很好地与跟踪模型参数反演结果相对应;(3)构建的监测点优化模型可以很好地实现排水管网监测点的优化布局。最后,利用污染源跟踪模型验证监测点优化布局的有效性,整个过程人工参与少,自动化程度高。本研究提出的自动化监测点优化布局模型已成功应用于实际案例,显著提高了城市排水管网监测效率,减少了人工参与程度,对提高城市水环境管理水平具有重要的现实意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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